Data Exploration and Visualisation
Visualisation is a key skill in your data science tool kit:
Rapidly explore data sets
Model evaluation and diagnostics
Sharing evidence
Telling compelling stories.
Reflective exercise, not a tutorial or rulebook.
Coffee consumption, visualised. Jaime Serra Palou.
Caffeination vs sleep, shown in lego. Elsie Lee-Robbins
{ggplot2}Layered creation of graphics from tidy data.
Learning {ggplot2}:
Use cases: exploratory analysis, presentation, report / paper, data journalism.
Considerations:
Bitmap Graphics: png, jpeg, gif
Vector Graphics: pdf, eps, svg
Who is the intended audience for your visualisation?
What knowledge do they bring with them?
What assumptions and biases do they hold?
Creating personas for distinct user groups can be helpful.
Issues with scales, area and perspective
Captions
Describes a figure or table so that it may be identified in a list of figures and (where appropriate).
Alternative text
Describes the content of an image for a person who cannot view it. (Guide to writing alt-text)
Titles
Give additional context or identify key findings. Active titles are preferable.
Graph to show how X varies with Y
Decisions cost time, energy and money. (DRY)
Consider your design choices carefully and write down your decisions and reasoning. (DRY)
This will form the basis of your own style-guide for data visualisation.
The Pudding (learning resources)
Think about your tools
Think about your medium
Think about your audience
Think about your story
Think about your guidelines
The Climate Book - Penguin
Coffee Cup - Jaime Serra Palou
Lego coffee - Elsie Lee-Robbins via Twitter
Male Heights - patient.info
Desaturated colour scales - {viridis} documentation
Effective Data Science: EDAV - Visualisation - Zak Varty